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BMC Bioinformatics ; 16: 206, 2015 Jul 02.
Article in English | MEDLINE | ID: mdl-26134144

ABSTRACT

BACKGROUND: Protein sequence alignments and database search methods use standard scoring matrices calculated from amino acid substitution frequencies in general sets of proteins. These general-purpose matrices are not optimal to align accurately sequences with marked compositional biases, such as hydrophobic transmembrane regions found in membrane proteins. In this work, an amino acid substitution matrix (GPCRtm) is calculated for the membrane spanning segments of the G protein-coupled receptor (GPCR) rhodopsin family; one of the largest transmembrane protein family in humans with great importance in health and disease. RESULTS: The GPCRtm matrix reveals the amino acid compositional bias distinctive of the GPCR rhodopsin family and differs from other standard substitution matrices. These membrane receptors, as expected, are characterized by a high content of hydrophobic residues with regard to globular proteins. On the other hand, the presence of polar and charged residues is higher than in average membrane proteins, displaying high frequencies of replacement within themselves. CONCLUSIONS: Analysis of amino acid frequencies and values obtained from the GPCRtm matrix reveals patterns of residue replacements different from other standard substitution matrices. GPCRs prioritize the reactivity properties of the amino acids over their bulkiness in the transmembrane regions. A distinctive role is that charged and polar residues seem to evolve at different rates than other amino acids. This observation is related to the role of the transmembrane bundle in the binding of ligands, that in many cases involve electrostatic and hydrogen bond interactions. This new matrix can be useful in database search and for the construction of more accurate sequence alignments of GPCRs.


Subject(s)
Amino Acid Substitution , Amino Acids/chemistry , Membrane Proteins/chemistry , Receptors, G-Protein-Coupled/chemistry , Rhodopsin/chemistry , Amino Acid Sequence , Computational Biology , Humans , Models, Molecular , Molecular Sequence Data , Phylogeny , Sequence Alignment , Sequence Homology, Amino Acid
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